scholarly journals A novel approach for fast and effective influence spread maximization in social networks

2020 ◽  
Author(s):  
Paolo Scarabaggio ◽  
Raffaele Carli ◽  
Mariagrazia Dotoli
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Influence Maximization ◽  
Viral Marketing ◽  
Computational Time ◽  
Maximization Problem ◽  
Influence Spread ◽  
The Social ◽  
Spread Of Influence ◽  
Influence Maximization Problem

The main characteristic of social networks is their ability to quickly spread information between a large group of people. This phenomenon is generated by the social influence that individuals induce on each other.<br>The widespread use of online social networks (e.g., Facebook) increases researchers' interest in how influence propagates through these networks. One of the most important research issues in this field is the so-called influence maximization problem, which essentially consists in selecting the most influential users (i.e., those who are able to maximize the spread of influence through the social network).<br>Due to its practical importance in various applications (e.g., viral marketing, target advertisement, personalized recommendation), such a problem has been studied in several variants. Different solution methodologies have been proposed. Nevertheless, the current open challenge in the resolution of the influence maximization problem still concerns achieving a good trade-off between accuracy and computational time. <br>In this context, based on the well-known independent cascade and the linear threshold models of social networks, we propose a novel low-complexity and highly accurate algorithm for selecting an initial group of nodes to maximize the spread of influence in large-scale networks. In particular, the key idea consists in iteratively removing the overlap of influence spread induced by different seed nodes. Application to several numerical experiments based on real datasets proves that the proposed algorithm effectively finds practical near-optimal solutions of the addressed influence maximization problem in a computationally efficient fashion. Finally, comparison with the best performing state of the art algorithms demonstrates that in large scale scenarios, the proposed approach shows higher performance in terms of influence spread and running time.

scholarly journals A novel approach for fast and effective influence spread maximization in social networks

2020 ◽  
Author(s):  
Paolo Scarabaggio ◽  
Raffaele Carli ◽  
Mariagrazia Dotoli
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Influence Maximization ◽  
Viral Marketing ◽  
Computational Time ◽  
Maximization Problem ◽  
Influence Spread ◽  
The Social ◽  
Spread Of Influence ◽  
Influence Maximization Problem

The main characteristic of social networks is their ability to quickly spread information between a large group of people. This phenomenon is generated by the social influence that individuals induce on each other.<br>The widespread use of online social networks (e.g., Facebook) increases researchers' interest in how influence propagates through these networks. One of the most important research issues in this field is the so-called influence maximization problem, which essentially consists in selecting the most influential users (i.e., those who are able to maximize the spread of influence through the social network).<br>Due to its practical importance in various applications (e.g., viral marketing, target advertisement, personalized recommendation), such a problem has been studied in several variants. Different solution methodologies have been proposed. Nevertheless, the current open challenge in the resolution of the influence maximization problem still concerns achieving a good trade-off between accuracy and computational time. <br>In this context, based on the well-known independent cascade and the linear threshold models of social networks, we propose a novel low-complexity and highly accurate algorithm for selecting an initial group of nodes to maximize the spread of influence in large-scale networks. In particular, the key idea consists in iteratively removing the overlap of influence spread induced by different seed nodes. Application to several numerical experiments based on real datasets proves that the proposed algorithm effectively finds practical near-optimal solutions of the addressed influence maximization problem in a computationally efficient fashion. Finally, comparison with the best performing state of the art algorithms demonstrates that in large scale scenarios, the proposed approach shows higher performance in terms of influence spread and running time.

scholarly journals Influence Maximization in Social Network Considering Memory Effect and Social Reinforcement Effect

2019 ◽  
Vol 11 (4) ◽  
pp. 95
Author(s):  
Wang ◽  
Zhu ◽  
Liu ◽  
Wang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Memory Effect ◽  
Influence Maximization ◽  
Viral Marketing ◽  
Maximization Problem ◽  
Social Reinforcement ◽  
Reinforcement Effect ◽  
The Social ◽  
Influence Maximization Problem

Social networks have attracted a lot of attention as novel information or advertisement diffusion media for viral marketing. Influence maximization describes the problem of finding a small subset of seed nodes in a social network that could maximize the spread of influence. A lot of algorithms have been proposed to solve this problem. Recently, in order to achieve more realistic viral marketing scenarios, some constrained versions of influence maximization, which consider time constraints, budget constraints and so on, have been proposed. However, none of them considers the memory effect and the social reinforcement effect, which are ubiquitous properties of social networks. In this paper, we define a new constrained version of the influence maximization problem that captures the social reinforcement and memory effects. We first propose a novel propagation model to capture the dynamics of the memory and social reinforcement effects. Then, we modify two baseline algorithms and design a new algorithm to solve the problem under the model. Experiments show that our algorithm achieves the best performance with relatively low time complexity. We also demonstrate that the new version captures some important properties of viral marketing in social networks, such as such as social reinforcements, and could explain some phenomena that cannot be explained by existing influence maximization problem definitions.

A survey on meta-heuristic algorithms for the influence maximization problem in the social networks

Computing ◽  
2021 ◽  
Author(s):  
Zahra Aghaee ◽  
Mohammad Mahdi Ghasemi ◽  
Hamid Ahmadi Beni ◽  
Asgarali Bouyer ◽  
Afsaneh Fatemi
Keyword(s):  
Social Networks ◽  
Heuristic Algorithms ◽  
Influence Maximization ◽  
Maximization Problem ◽  
The Social ◽  
Influence Maximization Problem

scholarly journals Influence Maximization in Social Networks using Deterministic Crowding Algorithm

2019 ◽  
Vol 8 (11) ◽  
pp. 4258-4265
Keyword(s):  
Social Network ◽  
Information Diffusion ◽  
Influence Maximization ◽  
Maximization Problem ◽  
Linear Threshold ◽  
The Social ◽  
Spread Of Influence ◽  
Influential Users ◽  
High Influence ◽  
Influence Maximization Problem

In a social network the individuals connected to one another become influenced by one another, while some are more influential than others and able to direct groups of individuals towards a move, an idea and an entity. These individuals are named influential users. Attempt is made by the social network researchers to identify such individuals because by changing their behaviors and ideologies due to communications and the high influence on one another would change many others' behaviors and ideologies in a given community. In information diffusion models, at all stages, individuals are influenced by their neighboring people. These influences and impressions thereof are constructive in an information diffusion process. In the Influence Maximization problem, the goal is to finding a subset of individuals in a social network such that by activating them, the spread of influence is maximized. In this work a new algorithm is presented to identify most influential users under the linear threshold diffusion model. It uses explicit multimodal evolutionary algorithms. Four different datasets are used to evaluate the proposed method. The results show that the precision of our method in average is improved 4.8% compare to best known previous works.

Scalable Influence Maximization Meets Efficiency and Effectiveness in Large-Scale Social Networks

2020 ◽  
Vol 30 (08) ◽  
pp. 1079-1096
Author(s):  
Liqing Qiu ◽  
Shuang Zhang ◽  
Chunmei Gu ◽  
Xiangbo Tian
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Influence Maximization ◽  
Scalable Algorithm ◽  
Influence Spread ◽  
Efficiency And Effectiveness ◽  
Spread Of Influence ◽  
Influential Nodes ◽  
Comparison Algorithms

Influence maximization is a problem that aims to select top [Formula: see text] influential nodes to maximize the spread of influence in social networks. The classical greedy-based algorithms and their improvements are relatively slow or not scalable. The efficiency of heuristic algorithms is fast but their accuracy is unacceptable. Some algorithms improve the accuracy and efficiency by consuming a large amount of memory usage. To overcome the above shortcoming, this paper proposes a fast and scalable algorithm for influence maximization, called K-paths, which utilizes the influence tree to estimate the influence spread. Additionally, extensive experiments demonstrate that the K-paths algorithm outperforms the comparison algorithms in terms of efficiency while keeping competitive accuracy.

scholarly journals Community-based framework for influence maximization problem in social networks

2021 ◽  
Vol 24 (3) ◽  
pp. 1604
Author(s):  
Mustafa K. Alasadi ◽  
Ghusoon Idan Arb
Keyword(s):  
Community Structure ◽  
Social Network ◽  
Diffusion Process ◽  
Influence Maximization ◽  
Viral Marketing ◽  
Community Diversity ◽  
Maximization Problem ◽  
Special Role ◽  
The Social ◽  
Influence Maximization Problem

<p>Given a social graph, the influence maximization problem (IMP) is the act of selecting a group of nodes that cause maximum influence if they are considered as seed nodes of a diffusion process. IMP is an active research area in social network analysis due to its practical need in applications like viral marketing, target advertisement, and recommendation system. In this work, we propose an efficient solution for IMP based on the social network structure. The community structure is a property of real-world graphs. In fact, communities are often overlapping because of the involvement of users in many groups (family, workplace, and friends). These users are represented by overlapped nodes in the social graphs and they play a special role in the information diffusion process. This fact prompts us to propose a solution framework consisting of three phases: firstly, the community structure is discovered, secondly, the candidate seeds are generated, then lastly the set of final seed nodes are selected. The aim is to maximize the influence with the community diversity of influenced users. The study was validated using synthetic as well as real social network datasets. The experimental results show improvement over baseline methods and some important conclusions were reported.</p>

scholarly journals Influence maximization in social networks

2019 ◽  
Author(s):  
◽  
Ghinwa Bou Matar
Keyword(s):  
Social Networks ◽  
Seed Set ◽  
Influence Maximization ◽  
Viral Marketing ◽  
Maximization Problem ◽  
Influence Propagation ◽  
Influence Model ◽  
Main Challenge ◽  
Influence Spread ◽  
Total Influence

The main challenge in viral marketing, that is powered by social networks, is to minimize the seed set that will initiate the diffusion process and maximize the total influence at its termination. The aim of this thesis is to study influence propagation models under the influence maximization problem and to investigate the effectiveness of a new model that is based on a multi-objective approach. We propose a Depth-Based Diminishing Influence model (DBDM) that is based on adding nodes to the seed set by considering influenced in-neighbors and how far these in-neighbors are from the initial activated set. As an enhancement to our approach, we used a clustering mechanism to help increase the influence spread. Several experiments were conducted to compare between our approach and previous work. As a result, the selection of the seed set under the DBDM model boosted the influence spread substantially compared to previously proposed models.

scholarly journals On the Shoulders of Giants: Incremental Influence Maximization in Evolving Social Networks

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Xiaodong Liu ◽  
Xiangke Liao ◽  
Shanshan Li ◽  
Si Zheng ◽  
Bin Lin ◽  
...  
Keyword(s):  
Social Networks ◽  
Real World ◽  
Search Space ◽  
Influence Maximization ◽  
Viral Marketing ◽  
Maximization Problem ◽  
Dynamic Social Networks ◽  
Influence Spread ◽  
The Impact ◽  
Influential Individuals

Influence maximization problem aims to identify the most influential individuals so as to help in developing effective viral marketing strategies over social networks. Previous studies mainly focus on designing efficient algorithms or heuristics on a static social network. As a matter of fact, real-world social networks keep evolving over time and a recalculation upon the changed network inevitably leads to a long running time. In this paper, we propose an incremental approach, IncInf, which can efficiently locate the top-K influential individuals in evolving social networks based on previous information instead of calculation from scratch. In particular, IncInf quantitatively analyzes the influence spread changes of nodes by localizing the impact of topology evolution to only local regions, and a pruning strategy is further proposed to narrow the search space into nodes experiencing major increases or with high degrees. To evaluate the efficiency and effectiveness, we carried out extensive experiments on real-world dynamic social networks: Facebook, NetHEPT, and Flickr. Experimental results demonstrate that, compared with the state-of-the-art static algorithm, IncInf achieves remarkable speedup in execution time while maintaining matching performance in terms of influence spread.

scholarly journals RLIM: Representation Learning Method for Influence Maximization in social networks

2021 ◽  
Author(s):  
Sun Chengai ◽  
Duan Xiuliang ◽  
Qiu Liqing ◽  
Shi Qiang ◽  
Li Tengteng
Keyword(s):  
Network Architecture ◽  
Information Diffusion ◽  
Representation Learning ◽  
Influence Maximization ◽  
Maximization Problem ◽  
Learning Method ◽  
Neural Network Architecture ◽  
Influence Spread ◽  
Information Diffusion Model ◽  
Influence Maximization Problem

Abstract A core issue in influence propagation is influence maximization, which aims to find a group of nodes under a specific information diffusion model and maximize the final influence of this group of nodes. The limitation of the existing researches is that they excessively depend on the information diffusion model and randomly set the propagation ability (probability). Therefore, most of the algorithms for solving the influence maximization problem are basically difficult to expand in large social networks. Another challenge is that fewer researchers have paid attention to the problem of the large difference between the estimated influence spread and the actual influence spread. A measure to solve the influence maximization problem is applying advanced neural network architecture also represents learning method. Based on this idea, the paper proposes Representation Learning for Influence Maximization (RLIM) algorithm. The premise of this algorithm is to construct the influence cascade of each source node. The key is to adopt neural network architecture to realize the prediction of propagation ability. The purpose is to apply the propagation ability to the influence maximization problem by representation learning. Furthermore, the results of the experiments show that RLIM algorithm has greater diffusion ability than the state-of-the-art algorithms on different online social network data sets, and the diffusion of information is more accurate.

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